Case Studies: E-Commerce Optimization
Case Studies: E-Commerce Optimization
Generative engines have revolutionized e-commerce by providing context-aware recommendations. Instead of listing items, AI suggests complete outfits or product bundles based on user preferences.
Example:
A Bangladeshi fashion retailer integrates AI to recommend complete festive outfits rather than individual garments.
AI considers user history, season, and style compatibility.
Exercise:
Select 10 products from your catalog.
Generate AI-recommended bundles.
Compare relevance and customer appeal against manual recommendations.
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Case Studies: News Summarization
AI-generated summaries can help news portals increase engagement:
Extract key entities
Summarize events concisely
Suggest related stories
Demo:
Pick 5 current news articles.
Generate AI summaries and highlight top entities.
Measure readability and factual accuracy.
Exercise:
Compare AI summaries with human-written summaries.
Identify strengths and weaknesses.
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Case Studies: Knowledge Management
Enterprise knowledge bases benefit from GEO:
Semantic search improves internal Q&A
Structured documents reduce time to find answers
AI-powered suggestions help onboarding
Demo:
Create a mini knowledge graph for 10 internal processes.
Query AI to answer questions about each process.
Exercise:
Record AI response accuracy and completeness.
Optimize entity relationships for better AI comprehension.
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Metrics: Tracking AI Performance
GEO success can be measured using:
1. AI recognition of products/brands
2. Entity accuracy
3. User engagement from AI outputs
4. Citation reliability
Exercise:
Monitor AI mentions of 5 sample products over a week.
Track accuracy, relevance, and completeness.
Demo:
Build a spreadsheet recording AI responses for different queries.
Highlight discrepancies.
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Metrics: Dashboards for GEO
Visual dashboards allow brands to monitor AI performance:
Identify top recognized products
Track trends over time
Compare AI recommendations with sales metrics
Demo:
Build a dashboard showing AI product recognition for top 10 products.
Overlay with actual sales and user engagement data.
Exercise:
Design a dashboard to monitor AI citation trends.
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Advanced Topic: Bias and Fairness
GEO requires ethical oversight:
Avoid AI bias in recommendations
Ensure representation across categories
Monitor for hallucinated or misleading outputs
Exercise:
Review AI product recommendations for bias in 5 categories.
Document observations and propose mitigation strategies.
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Advanced Topic: AI Content Creation
AI can generate:
Blogs
Social media posts
Product descriptions
Tutorials
Example:
Generate AI blog posts for a new lipstick line, focusing on shades, finishes, and target demographics.
Exercise:
Evaluate AI-generated content for brand tone, accuracy, and engagement potential.
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Advanced Topic: Predictive Recommendations
Generative engines can predict user needs:
Suggest complementary products proactively
Personalize recommendations using past behavior
Demo:
A user views matte lipsticks. AI suggests matching glosses and skincare.
Exercise:
Simulate predictive product recommendations for 10 user profiles.
Measure AI accuracy and relevance.
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Integration: APIs and Embeddings
APIs expose structured data for AI consumption:
JSON-LD and structured endpoints help AI understand products
Embeddings store semantic representations in vector databases
Demo:
Build a small API serving product details
Query AI for recommendations using RAG pipelines
Exercise:
Compare AI outputs using structured API data versus unstructured text
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Page 30 – Integration: Knowledge Graphs
Knowledge graphs represent entities and relationships:
Entities: products, brands, categories
Relationships: suitable for, made in, part of
Example:
Lipstick → Finish → Matte
Lipstick → Brand → Lafz
Lafz → Country → Italy
Exercise:
Build a knowledge graph for 10 products
Query AI and verify accuracy of responses


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